Load Libraries
library(tidyverse)
## -- Attaching packages -------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.0 v purrr 0.3.4
## v tibble 3.0.0 v dplyr 0.8.5
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts ----------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
data = readRDS("../../data/data.rds")
str(data)
## 'data.frame': 75866 obs. of 22 variables:
## $ Race : chr "10M" "10M" "10M" "10M" ...
## $ Name : chr "Jane Omoro " "Jane Ngotho " "Lidiya Grigoryeva " "Eunice Sagero " ...
## $ Gender : chr "W" "W" "W" "W" ...
## $ Age : num 26 29 NA 20 29 24 38 NA 27 30 ...
## $ Time : POSIXct, format: "0000-01-01 00:53:37" "0000-01-01 00:53:38" ...
## $ Pace : POSIXct, format: "0000-01-01 00:05:22" "0000-01-01 00:05:22" ...
## $ PiS : num 1 2 3 4 5 6 7 8 9 10 ...
## $ TiS : num 2358 2358 2358 2358 2358 ...
## $ Division : chr "W2529" "W2529" "NR" "W2024" ...
## $ PiD : num 1 2 NA 1 3 2 1 NA 4 1 ...
## $ TiD : num 559 559 NA 196 559 196 387 NA 559 529 ...
## $ Hometown : chr "Kenya" "Kenya" "Russia" "Kenya" ...
## $ Home State : chr NA NA NA NA ...
## $ year : int 1999 1999 1999 1999 1999 1999 1999 1999 1999 1999 ...
## $ divisionTitle: chr "Overall+Women" "Overall+Women" "Overall+Women" "Overall+Women" ...
## $ section : chr "10M" "10M" "10M" "10M" ...
## $ page : int 1 1 1 1 1 1 1 1 1 1 ...
## $ link : chr "http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1" "http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1" "http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1" "http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1" ...
## $ DivisionCode : chr "W" "W" "N" "W" ...
## $ DivisionNum : chr "529" "529" "" "024" ...
## $ TimeMins : num 53.6 53.6 53.7 53.9 54.1 ...
## $ PaceMins : num 5.37 5.37 5.37 5.4 5.42 ...
summary(data)
## Race Name Gender Age
## Length:75866 Length:75866 Length:75866 Min. : 7.00
## Class :character Class :character Class :character 1st Qu.:27.00
## Mode :character Mode :character Mode :character Median :32.00
## Mean :33.85
## 3rd Qu.:39.00
## Max. :87.00
## NA's :20
## Time Pace PiS
## Min. :0000-01-01 00:51:44 Min. :0000-01-01 00:05:10 Min. : 1
## 1st Qu.:0000-01-01 01:28:39 1st Qu.:0000-01-01 00:08:52 1st Qu.:1356
## Median :0000-01-01 01:37:29 Median :0000-01-01 00:09:45 Median :2786
## Mean :0000-01-01 01:38:13 Mean :0000-01-01 00:09:50 Mean :3305
## 3rd Qu.:0000-01-01 01:46:58 3rd Qu.:0000-01-01 00:10:42 3rd Qu.:4905
## Max. :0000-01-01 02:57:31 Max. :0000-01-01 00:17:45 Max. :9729
##
## TiS Division PiD TiD
## Min. :2166 Length:75866 Min. : 1.0 Min. : 1
## 1st Qu.:4333 Class :character 1st Qu.: 165.0 1st Qu.: 559
## Median :6395 Mode :character Median : 404.0 Median : 953
## Mean :6609 Mean : 595.6 Mean :1190
## 3rd Qu.:8853 3rd Qu.: 816.0 3rd Qu.:1678
## Max. :9729 Max. :5302.0 Max. :2803
## NA's :20 NA's :20
## Hometown Home State year divisionTitle
## Length:75866 Length:75866 Min. :1999 Length:75866
## Class :character Class :character 1st Qu.:2005 Class :character
## Mode :character Mode :character Median :2008 Mode :character
## Mean :2007
## 3rd Qu.:2010
## Max. :2012
##
## section page link DivisionCode
## Length:75866 Min. : 1.0 Length:75866 Length:75866
## Class :character 1st Qu.: 68.0 Class :character Class :character
## Mode :character Median :140.0 Mode :character Mode :character
## Mean :165.7
## 3rd Qu.:246.0
## Max. :487.0
##
## DivisionNum TimeMins PaceMins
## Length:75866 Min. : 51.73 Min. : 5.167
## Class :character 1st Qu.: 88.65 1st Qu.: 8.867
## Mode :character Median : 97.48 Median : 9.750
## Mean : 98.22 Mean : 9.823
## 3rd Qu.:106.97 3rd Qu.:10.700
## Max. :177.52 Max. :17.750
##
head(data)
## Race Name Gender Age Time Pace
## 1 10M Jane Omoro W 26 0000-01-01 00:53:37 0000-01-01 00:05:22
## 2 10M Jane Ngotho W 29 0000-01-01 00:53:38 0000-01-01 00:05:22
## 3 10M Lidiya Grigoryeva W NA 0000-01-01 00:53:40 0000-01-01 00:05:22
## 4 10M Eunice Sagero W 20 0000-01-01 00:53:55 0000-01-01 00:05:24
## 5 10M Alla Zhilyayeva W 29 0000-01-01 00:54:08 0000-01-01 00:05:25
## 6 10M Teresa Wanjiku W 24 0000-01-01 00:54:10 0000-01-01 00:05:25
## PiS TiS Division PiD TiD Hometown Home State year divisionTitle section page
## 1 1 2358 W2529 1 559 Kenya <NA> 1999 Overall+Women 10M 1
## 2 2 2358 W2529 2 559 Kenya <NA> 1999 Overall+Women 10M 1
## 3 3 2358 NR NA NA Russia <NA> 1999 Overall+Women 10M 1
## 4 4 2358 W2024 1 196 Kenya <NA> 1999 Overall+Women 10M 1
## 5 5 2358 W2529 3 559 Russia <NA> 1999 Overall+Women 10M 1
## 6 6 2358 W2024 2 196 Kenya <NA> 1999 Overall+Women 10M 1
## link
## 1 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 2 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 3 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 4 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 5 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 6 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## DivisionCode DivisionNum TimeMins PaceMins
## 1 W 529 53.61667 5.366667
## 2 W 529 53.63333 5.366667
## 3 N 53.66667 5.366667
## 4 W 024 53.91667 5.400000
## 5 W 529 54.13333 5.416667
## 6 W 024 54.16667 5.416667
unique(data$Race)
## [1] "10M"
unique(data$Gender)
## [1] "W" "xue Zhu" "suzy" "Cindy"
unique(data %>% arrange(Age) %>% select(Age) %>% pluck(1))
## [1] 7 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
## [26] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58
## [51] 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 80 81 83 84 85
## [76] 86 87 NA
unique(data %>% arrange(Division) %>% select(Division) %>% pluck(1))
## [1] "NR" "W0119" "W2024" "W2529" "W3034" "W3539" "W4044" "W4549" "W5054"
## [10] "W5559" "W6064" "W6569" "W7074" "W7579" "W8099"
- Some issues with Gender –> Does it matter?
Missing Values
data %>%
select_if(function(x) any(is.na(x))) %>%
summarise_each(~ sum(is.na(.)))
## Age PiD TiD Home State
## 1 20 20 20 241
noresults = data %>% dplyr::filter(Division == "NR")
dim(noresults)
## [1] 19 22
head(noresults)
## Race Name Gender Age Time Pace
## 1 10M Lidiya Grigoryeva W NA 0000-01-01 00:53:40 0000-01-01 00:05:22
## 2 10M Gladys Asiba W NA 0000-01-01 00:54:50 0000-01-01 00:05:29
## 3 10M Connie Buckwalter W NA 0000-01-01 00:59:36 0000-01-01 00:05:58
## 4 10M Ann Reid W NA 0000-01-01 01:53:03 0000-01-01 00:11:18
## 5 10M Loretta Cuce W NA 0000-01-01 01:53:38 0000-01-01 00:11:22
## 6 10M Unidentified Runner W NA 0000-01-01 01:19:45 0000-01-01 00:07:59
## PiS TiS Division PiD TiD Hometown Home State year divisionTitle section
## 1 3 2358 NR NA NA Russia <NA> 1999 Overall+Women 10M
## 2 8 2358 NR NA NA Kenya <NA> 1999 Overall+Women 10M
## 3 17 2358 NR NA NA Lancaster PA 1999 Overall+Women 10M
## 4 2176 2358 NR NA NA Bethesda MD 1999 Overall+Women 10M
## 5 2611 2972 NR NA NA Alexandria VA 2001 Overall+Women 10M
## 6 270 3333 NR NA NA Washington DC 2002 Overall+Women 10M
## page
## 1 1
## 2 1
## 3 1
## 4 109
## 5 131
## 6 14
## link
## 1 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 2 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 3 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 4 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=109
## 5 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=2001&division=Overall+Women&page=131
## 6 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=2002&division=Overall+Women&page=14
## DivisionCode DivisionNum TimeMins PaceMins
## 1 N 53.66667 5.366667
## 2 N 54.83333 5.483333
## 3 N 59.60000 5.966667
## 4 N 113.05000 11.300000
## 5 N 113.63333 11.366667
## 6 N 79.75000 7.983333
noage = data %>% dplyr::filter(is.na(Age))
dim(noage)
## [1] 20 22
head(noage)
## Race Name Gender Age Time Pace
## 1 10M Lidiya Grigoryeva W NA 0000-01-01 00:53:40 0000-01-01 00:05:22
## 2 10M Gladys Asiba W NA 0000-01-01 00:54:50 0000-01-01 00:05:29
## 3 10M Connie Buckwalter W NA 0000-01-01 00:59:36 0000-01-01 00:05:58
## 4 10M Ann Reid W NA 0000-01-01 01:53:03 0000-01-01 00:11:18
## 5 10M Loretta Cuce W NA 0000-01-01 01:53:38 0000-01-01 00:11:22
## 6 10M Unidentified Runner W NA 0000-01-01 01:19:45 0000-01-01 00:07:59
## PiS TiS Division PiD TiD Hometown Home State year divisionTitle section
## 1 3 2358 NR NA NA Russia <NA> 1999 Overall+Women 10M
## 2 8 2358 NR NA NA Kenya <NA> 1999 Overall+Women 10M
## 3 17 2358 NR NA NA Lancaster PA 1999 Overall+Women 10M
## 4 2176 2358 NR NA NA Bethesda MD 1999 Overall+Women 10M
## 5 2611 2972 NR NA NA Alexandria VA 2001 Overall+Women 10M
## 6 270 3333 NR NA NA Washington DC 2002 Overall+Women 10M
## page
## 1 1
## 2 1
## 3 1
## 4 109
## 5 131
## 6 14
## link
## 1 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 2 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 3 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=1
## 4 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=1999&division=Overall+Women&page=109
## 5 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=2001&division=Overall+Women&page=131
## 6 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=2002&division=Overall+Women&page=14
## DivisionCode DivisionNum TimeMins PaceMins
## 1 N 53.66667 5.366667
## 2 N 54.83333 5.483333
## 3 N 59.60000 5.966667
## 4 N 113.05000 11.300000
## 5 N 113.63333 11.366667
## 6 N 79.75000 7.983333
setdiff(noage, noresults)
## Race Name Gender Age Time Pace PiS
## 1 10M Michelle Hinman W NA 0000-01-01 01:39:13 0000-01-01 00:09:55 2455
## TiS Division PiD TiD Hometown Home State year divisionTitle section page
## 1 4333 W8099 1 2 NR <NA> 2005 Overall+Women 10M 123
## link
## 1 http://www.cballtimeresults.org/performances?utf8=%E2%9C%93§ion=10M&year=2005&division=Overall+Women&page=123
## DivisionCode DivisionNum TimeMins PaceMins
## 1 W 099 99.21667 9.916667
- This seems to be an issue. It looks like this person is placed first in the division, but age is not set so she has been classified as W8099 and the time seems to be off from what would be expected from this age bracket.
- TODO: Fix this
# Remove no results
data = data %>%
dplyr::filter(Division != "NR")
Participants by Year
plotdata = data %>%
group_by(year) %>%
summarise(count=n())
p = plotdata %>%
ggplot(aes(x=year, y=count)) +
geom_line() +
geom_point()
ggplotly(p, tooltip="text")
p = plotdata %>%
ggplot(aes(x = year, y = count)) +
geom_bar(stat = "identity")
ggplotly(p, tooltip="text")
# Alternate colored by Division
plotdata = data %>%
group_by(year, Division) %>%
summarise(count = n())
p = plotdata %>%
ggplot(aes(x = year, y = count, fill = Division)) +
geom_bar(stat = "identity", position = "stack")
ggplotly(p, tooltip="text")
Age Brackets by Year
plotdata_by_year = data %>%
group_by(year) %>%
summarise(count_year = n())
plotdata_by_year_div = data %>%
group_by(year, Division) %>%
summarise(count_year_div = n())
plotdata = plotdata_by_year %>%
plyr::join(plotdata_by_year_div, by = "year", type = "full") %>%
mutate(percent = round(count_year_div/count_year*100,1))
plotdata %>%
ggplot(aes(x = year, y = percent, fill = Division)) +
geom_bar(stat = "identity", position = "stack")
Race Times
p = data %>%
mutate_at("year", as.factor) %>%
ggplot(aes(x=year, y=TimeMins)) +
geom_boxplot()
ggplotly(p, tooltip="text")
p = data %>%
dplyr::filter(!(Division %in% c("W8099", "W7579"))) %>%
mutate_at("year", as.factor) %>%
ggplot(aes(x=year, y=TimeMins, fill=year)) +
geom_boxplot() +
facet_wrap(. ~ Division, ncol=4) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
coord_flip()
p
#ggplotly(p, tooltip="text")
p = data %>%
dplyr::filter(Division %in% c("W2529", "W3034", "W3539", "W4044")) %>%
mutate_at("year", as.factor) %>%
ggplot(aes(x=year, y=TimeMins, fill=year)) +
geom_boxplot() +
facet_wrap(. ~ Division, ncol=4) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
coord_flip()
m <- list(
l = 100,
r = 50,
b = 100,
t = 50,
pad = 4
)
gp = ggplotly(p, tooltip="text", width=800, height=600) %>%
layout(
autosize = F,
margin=m,
legend = list(orientation = "h", xanchor = "center", x = 0.5, y = -0.15)
) %>%
style(legendgroup = NULL)
# gp
# find the annotation you want to move
# Based on https://stackoverflow.com/questions/42763280/r-ggplot-and-plotly-axis-margin-wont-change
labels = c("year", "TimeMins")
for(i in seq_along(gp[['x']][['layout']][['annotations']])){
for(label in labels){
if (gp[['x']][['layout']][['annotations']][[i]]$text == label){
print(paste(
label, "Index: ", i, "X, Y: ",
gp[['x']][['layout']][['annotations']][[i]]$x,
gp[['x']][['layout']][['annotations']][[i]]$y)
)
}
}
}
## [1] "TimeMins Index: 1 X, Y: 0.5 -0.0356164383561644"
## [1] "year Index: 2 X, Y: -0.0321917808219178 0.5"
## [1] "year Index: 7 X, Y: 1.02 1"
# X Label
gp[['x']][['layout']][['annotations']][[1]]$y = -0.1
# Y Label
gp[['x']][['layout']][['annotations']][[2]]$x = -0.075
# Legend XY
gp[['x']][['layout']][['annotations']][[7]]$x = -0.025
gp[['x']][['layout']][['annotations']][[7]]$y = -0.225
gp